Student’s t-Tests

Kalpić, Damir; Hlupić, Nikica; Lovrić, Miodrag

doi:10.1007/978-3-642-04898-2_641

Cited by 79 publications

(65 citation statements)

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“…The Student's t-test was applied to check the statistical significance of the experiment. This test is used for verifying a statistical hypothesis about the mean of a normal population with dependent samples of a relatively small size [26]. As the data is dependent-i.e., all the participants played both versions but in different order-the Student's t-test was split in two parts: in the first the participants played first the BG and then the NBG versions and in the second played the reversed order.…”

Section: Resultsmentioning

confidence: 99%

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Promoting Optimal User Experience through Composite Challenge Tasks

Cruz

Pineda

2019

Applied Sciences

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Optimal user experience or flow is a theory with great impact on user experience. Promoting flow has become a competitive advantage for interactive systems, including rehabilitation. This can be achieved through an engaging interface that provides a rewarding experience and motivates the user to use the system again. This theory sustains that promoting a state of flow and improving task performance depends heavily on the balance between the challenges posed by the system and the skills deployed by the user. We further claim that balanced mental and motor skills demanded by the task improve flow and task performance. This paper presents an experiment supporting these claims. For this, we built two movement-interaction rehabilitation systems called SIBMER and Macoli (arm in Náhuatl). Both systems have two versions, one with a balanced load of mental and motor skills, and the other with an unbalanced one. Both versions are compared in terms of their potential to promote the state of flow and to improve task performance. Results show that a balance demand of mental and motor skills promotes flow, independently of the task complexity. Likewise, the experiment shows a correlation between flow and performance.

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Section: Resultsmentioning

confidence: 99%

“…The results with the values of the means for the global flow score are shown in Table 13 where the global BG mean is greater than the global NBG mean. The Student's t-test for independent samples is used to validate whether this result is significant [26]. The Student's t-test for independent samples assumes the homogeneity of the variance.…”

Section: Resultsmentioning

confidence: 99%

Promoting Optimal User Experience through Composite Challenge Tasks

Cruz

Pineda

2019

Applied Sciences

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“…For box plots, data points considered 'outliers' were determined based on criteria defined in the Grubbs outlier test 69 and omitted. ANOVA tests with Bonferroni's post-tests 70,71 were used to determine the statistical significance of experiments with more than two samples, whereas Student's t tests were performed to assess statistical differences between samples 72 . Significance is given as * P < 0.05; * * P < 0.01; * * * P < 0.001.…”

Section: Discussionmentioning

confidence: 99%

α-Synuclein plasma membrane localization correlates with cellular phosphatidylinositol polyphosphate levels

Jacob

Dema

Mercadante

et al. 2020

Preprint

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The Parkinson′s disease protein α-synuclein (αSyn) promotes membrane fusion and fission by interacting with various negatively charged phospholipids. Despite postulated roles in endocytosis and exocytosis, plasma membrane (PM) interactions of αSyn are poorly understood. Here, we show that phosphatidylinositol 4,5-bisphosphate (PIP2) and phosphatidylinositol 3,4,5-trisphosphate (PIP3), two highly acidic components of inner PM leaflets, mediate plasma membrane localization of endogenous pools of αSyn in A2780, HeLa, SH-SY5Y and SK-MEL-2 cells. We demonstrate that αSyn binds reconstituted PIP2-membranes in a helical conformation in vitro and that PIP2 kinases and phosphatases reversibly redistribute αSyn in cells. We further delineate that αSyn-PM targeting follows phosphoinositide-3 kinase (PI3K)-dependent changes of cellular PIP2 and PIP3 levels, which collectively suggests that phosphatidylinositol polyphosphates contribute to αSyn′s cellular function(s) at the plasma membrane.

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“…In this research, five hypothesis tests including the t-test (Kalpić et al, 2011), the rank-sum test, Signed-rank test (Wilcoxon, 1945), the KS-test (Newman, 1939), and the F-test (Snedecor and Cochran, 1989) were used to determine whether there was a statistically significant difference between the modeled output and observed DO, as well as to compared the two models. The 5% significance level was used to determine if the results were significantly different or not.…”

Section: Hypothesis Testing For Model Comparisonmentioning

confidence: 99%

A Comparison of Two Data-Driven Models to Predict Hypolimnetic Dissolved Oxygen Concentration: A Case Study of the Seymareh Reservoir in Iran

Nokhandan¹,

Snieder²,

Khan

et al. 2019

J ENVIRON INFORM LET

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Dissolved oxygen concentration (DO) is a crucial factor in maintaining aquatic ecosystem health. In this research, two data-driven modelling (DDM) techniques, multiple linear regression (MLR) and artificial neural networks (ANN), were developed, implemented and compared to predict the DO in the hypolimnetic layer of Seymareh Reservoir in Iran. Low DO in this Reservoir lead to a fish kill event and thus, this reservoir is of interest to water managers in the region. Water quality monitoring data from the Reservoir and an upstream river were used for training the models. In addition, two input variable selection methods, linear correlation analysis and combined neural pathway strength analysis (CNPSA, a nonlinear variable selection method) were developed and compared to determine the most significant inputs to predict hypolimnetic DO. A systematic method to select the optimum architecture of the network is proposed and tested. While these two approaches have been investigated previously, this research focuses on creating a systematic approach to combining two sources of uncertainty of DDM models. Additionally, the performance of CNPSA has not been compared to linear variable selection techniques. This research demonstrates the importance of using systematic input selection and network design for improved DO prediction in a large Reservoir. The performance of the models was quantified using the Nash-Sutcliffe efficiency and root mean squared error, which demonstrated that the ANN approach had better performance compared to the MLR model. The approach demonstrates that by using a systematic input variable selection approach combined with an optimised network architecture, a high performance of DO prediction can be achieved using easily measured upstream input data.

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Student’s t-Tests

Cited by 79 publications

References 0 publications

Promoting Optimal User Experience through Composite Challenge Tasks

Promoting Optimal User Experience through Composite Challenge Tasks

α-Synuclein plasma membrane localization correlates with cellular phosphatidylinositol polyphosphate levels

A Comparison of Two Data-Driven Models to Predict Hypolimnetic Dissolved Oxygen Concentration: A Case Study of the Seymareh Reservoir in Iran

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